Multiple-input multiple output (MIMO) communication systems have been shown to provide improvements in capacity and reliability over single-input single-output (SISO) communication systems. The MIMO communication systems commonly employ a block structure wherein a MIMO transmitter (which is a cooperating collection of single-dimension transmitters) sends a vector of symbol information. This symbol vector may represent one or more coded or uncoded SISO data symbols. A MIMO receiver (which is a cooperating collection of single-dimension receivers) receives one or more copies of this transmitted vector of symbol information. The performance of the entire communication system hinges on the ability of the receiver to find reliable estimates of the symbol vector that the transmitter transmitted.
Several standards have been established to provide uniformity and support growth in the development of wireless networks. One such standard that has been promulgated by the Institute of Electrical and Electronic Engineers (IEEE) is IEEE 802.11, which is incorporated herein by reference in its entirety. IEEE 802.11 is an umbrella standard that encompasses a family of specifications pertaining to wireless communication. Generally, IEEE 802.11 specifies an over-the-air interface between a wireless client and a base station or between two wireless clients.
There are several specifications within the IEEE 802.11 family covering topics such as different transmission rates, encoding schemes and frequency bands for transmitting data wirelessly. For example, IEEE 802.11(a) is an extension of IEEE 802.11 that specifically addresses wireless local area networks (WLANs) having a data rate up to 54 Mbps and employing a carrier frequency of 2.4/5 GHz. IEEE 802.11(a) specifies for such WLANs an orthogonal frequency division multiplexing (OFDM) encoding scheme for the vectors of symbol information.
A 2×2 MIMO communication system conforming to the IEEE 802.11(a) standard may transmit two independent and concurrent signals, employing two single-dimension transmitters having separate transmit antennas and two single-dimension receivers having separate receive antennas. Alternatively, the antennas could be derived from a single physical antenna that appropriately employs polarization. Two receive signals Y1(k), Y2(k) on the kth sub-carrier following a Fast Fourier Transformation and assuming negligible inter-symbol interference may be written as:Y1(k)=H11(k)*X1(k)+H12(k)*X2(k)+n1(k)Y2(k)=H21(k)*X1(k)+H22(k)*X2(k)+n2(k)where X1(k) and X2(k) are two independent signals transmitted on the kth sub-carrier from the first and second transmit antennas, respectively, and n1 and n2 are noises associated with the two receive signals. The term Hij(k), where i=1, 2 and j=1, 2, incorporates gain and phase distortion associated with symbols transmitted on the kth sub-carrier from transmit antenna j to receive antenna i. The channel gain and phase terms Hij(k) may also include gain and phase distortions due to signal conditioning stages such as filters and other analog electronics. The receiver requires that the channel values Hij(k) reliably decode the transmitted signals X1(k) and X2(k).
In order to estimate the channel coefficients Hij(k) at the receiver, the transmitter and the receiver employ training sequences. These training sequences are predetermined and known at both the transmitter and the receiver. In IEEE 802.11(a), a long training sequence is employed as part of a preamble to the transmission of data. This long sequence involves the transmission of a known sequence of vector symbols, employing 52 excited tones (1 or −1) and an unexcited tone (0) both at DC and at each end of the spectrum, to provide a guard interval that is used to reduce inter-symbol interference. An appropriate calculation of individual channel estimates H11(k), H12(k), H21(k), H22(k) may typically require a processor employing complex calculations. Therefore, a trade-off usually exists in achieving a reliable channel estimate between the quality of channel estimation and the cost related to computational complexity. What is needed in the art is a way to enhance quality of channel estimation while reducing computational complexity.